Biomarker development in the precision medicine era: lung cancer as a case study

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Background

The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

Methods

We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.

Findings

A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.

Interpretation

The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.

Funding

No funding received.

Tumor biomarkers for diagnosis, prognosis and targeted therapy

Tumor biomarkers, the substances which are produced by tumors or the body's responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.

Fluorescent Probes for Disease Diagnosis

The identification and detection of disease-related biomarkers is essential for early clinical diagnosis, evaluating disease progression, and for the development of therapeutics. Possessing the advantages of high sensitivity and selectivity, fluorescent probes have become effective tools for monitoring disease-related active molecules at the cellular level and in vivo. In this review, we describe current fluorescent probes designed for the detection and quantification of key bioactive molecules associated with common diseases, such as organ damage, inflammation, cancers, cardiovascular diseases, and brain disorders. We emphasize the strategies behind the design of fluorescent probes capable of disease biomarker detection and diagnosis and cover some aspects of combined diagnostic/therapeutic strategies based on regulating disease-related molecules. This review concludes with a discussion of the challenges and outlook for fluorescent probes, highlighting future avenues of research that should enable these probes to achieve accurate detection and identification of disease-related biomarkers for biomedical research and clinical applications.

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives

There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort-however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics.

Saliva‑microbiome‑derived signatures: expected to become a potential biomarker for pulmonary nodules (MCEPN-1)

BACKGROUND

Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown.

METHODS

We conducted a 'Microbiome with pulmonary nodule series study 1' (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future. We performed 16 S rRNA amplicon sequencing on saliva samples from 173 PN patients and 40 HCs to compare the characteristics and functional changes in oral microbiota between the two groups. The random forest algorithm was used to identify PN salivary microbial markers. Biological functions and potential mechanisms of differential genes in saliva samples were preliminarily explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups (COG) analyses.

RESULTS

The diversity of salivary microorganisms was higher in the PN group than in the HC group. Significant differences were noted in community composition and abundance of oral microorganisms between the two groups. Neisseria, Prevotella, Haemophilus and Actinomyces, Porphyromonas, Fusobacterium, 7M7x, Granulicatella and Selenomonas were the main differential genera between the PN and HC groups. Fusobacterium, Porphyromonas, Parvimonas, Peptostreptococcus and Haemophilus constituted the optimal marker sets (area under curve, AUC = 0.80), which can distinguish between patients with PNs and HCs. Further, the salivary microbiota composition was significantly correlated with age, sex, and smoking history (P < 0.001), but not with personal history of cancer (P > 0.05). Bioinformatics analysis of differential genes showed that patients with PN showed significant enrichment in protein/molecular functions related to immune deficiency and energy metabolisms, such as the cytoskeleton protein RodZ, nicotinamide adenine dinucleotide phosphate dehydrogenase (NADPH) dehydrogenase, major facilitator superfamily transporters and AraC family transcription regulators.

CONCLUSIONS

Our study provides the first evidence that the salivary microbiota can serve as potential biomarkers for identifying PN. We observed a significant association between changes in the oral microbiota and PNs, indicating the potential of salivary microbiota as a new non-invasive biomarker for PNs.

TRIAL REGISTRATION

Clinical trial registration number: ChiCTR2200062140; Date of registration: 07/25/2022.

Association of metabolomics with PD-1 inhibitor plus chemotherapy outcomes in patients with advanced non-small-cell lung cancer

BACKGROUND

Combining immune checkpoint inhibitors (ICIs) with chemotherapy has become a standard treatment for patients with non-small cell lung cancer (NSCLC) lacking driver gene mutations. Reliable biomarkers are essential for predicting treatment outcomes. Emerging evidence from various cancers suggests that early assessment of serum metabolites could serve as valuable biomarkers for predicting outcomes. This study aims to identify metabolites linked to treatment outcomes in patients with advanced NSCLC undergoing first-line or second-line therapy with programmed cell death 1 (PD-1) inhibitors plus chemotherapy.

METHOD

200 patients with advanced NSCLC receiving either first-line or second-line PD-1 inhibitor plus chemotherapy, and 50 patients undergoing first-line chemotherapy were enrolled in this study. The 200 patients receiving combination therapy were divided into a Discovery set (n=50) and a Validation set (n=150). These sets were further categorized into respond and non-respond groups based on progression-free survival PFS criteria (PFS≥12 and PFS<12 months). Serum samples were collected from all patients before treatment initiation for untargeted metabolomics analysis, with the goal of identifying and validating biomarkers that can predict the efficacy of immunotherapy plus chemotherapy. Additionally, the validated metabolites were grouped into high and low categories based on their medians, and their relationship with PFS was analyzed using Cox regression models in patients receiving combination therapy.

RESULTS

After the impact of chemotherapy was accounted for, two significant differential metabolites were identified in both the Discovery and Validation sets: N-(3-Indolylacetyl)-L-alanine and methomyl (VIP>1 and p<0.05). Notably, upregulation of both metabolites was observed in the group with a poorer prognosis. In the univariate analysis of PFS, lower levels of N-(3-Indolylacetyl)-L-alanine were associated with longer PFS (HR=0.59, 95% CI, 0.41 to 0.84, p=0.003), and a prolonged PFS was also indicated by lower levels of methomyl (HR=0.67, 95% CI, 0.47 to 0.96, p=0.029). In multivariate analyses of PFS, lower levels of N-(3-Indolylacetyl)-L-alanine were significantly associated with a longer PFS (HR=0.60, 95% CI, 0.37 to 0.98, p=0.041).

CONCLUSION

Improved outcomes were associated with lower levels of N-(3-Indolylacetyl)-L-alanine in patients with stage IIIB-IV NSCLC lacking driver gene mutations, who underwent first-line or second-line therapy with PD-1 inhibitors combined with chemotherapy. Further exploration of the potential predictive value of pretreatment detection of N-(3-Indolylacetyl)-L-alanine in peripheral blood for the efficacy of combination therapy is warranted.

STATEMENT

The combination of ICIs and chemotherapy has established itself as the new standard of care for first-line or second-line treatment in patients with advanced NSCLC lacking oncogenic driver alterations. Therefore, identifying biomarkers that can predict the efficacy and prognosis of immunotherapy plus chemotherapy is of paramount importance. Currently, the only validated predictive biomarker is programmed cell death ligand-1 (PD-L1), but its predictive value is not absolute. Our study suggests that the detection of N-(3-Indolylacetyl)-L-alanine in patient serum with untargeted metabolomics prior to combined therapy may predict the efficacy of treatment. Compared with detecting PD-L1 expression, the advantage of our biomarker is that it is more convenient, more dynamic, and seems to work synergistically with PD-L1 expression.

Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene sets

Background

Lung adenocarcinoma (LUAD) has a complex tumor heterogeneity. Our research attempts to clearness LUAD subtypes and build a reliable prognostic signature according to the activity changes of the hallmark and immunologic gene sets.

Methods

According to The Cancer Genome Atlas (TCGA) - LUAD dataset, changes in marker and immune gene activity were analyzed, followed by identification of prognosis-related differential gene sets (DGSs) and their related LUAD subtypes. Survival analysis, correlation with clinical characteristics, and immune microenvironment assessment for subtypes were performed. Moreover, the differentially expressed genes (DEGs) between different subtypes were identified, followed by the construction of a prognostic risk score (RS) model and nomogram model. The tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) of different risk groups were compared.

Results

Two LUAD subtypes were determined according to the activity changes of the hallmark and immunologic gene sets. Cluster 2 had worse prognosis, more advanced tumor and clinical stages than cluster 1. Moreover, a prognostic RS signature was established using two LUAD subtype-related DEGs, which could stratify patients at different risk levels. Nomogram model incorporated RS and clinical stage exerted good prognostic performance in LUAD patients. A shorter survival time and higher TMB were observed in the high-risk patients.

Conclusions

Our findings revealed that our constructed prognostic signature could exactly predict the survival status of LUAD cases, which was helpful in predicting the prognosis and guiding personalized therapeutic strategies for LUAD.

Scalable search of massively pooled nucleic acid samples enabled by a molecular database query language

The surge in nucleic acid analytics requires scalable storage and retrieval systems akin to electronic databases used to organize digital data. Such a system could transform disease diagnosis, ecological preservation, and molecular surveillance of biothreats. Current storage systems use individual containers for nucleic acid samples, requiring single-sample retrieval that falls short compared with digital databases that allow complex and combinatorial data retrieval on aggregated data. Here, we leverage protective microcapsules with combinatorial DNA labeling that enables arbitrary retrieval on pooled biosamples analogous to Structured Query Languages. Ninety-six encapsulated pooled mock SARS-CoV-2 genomic samples barcoded with patient metadata are used to demonstrate queries with simultaneous matches to sample collection date ranges, locations, and patient health statuses, illustrating how such flexible queries can be used to yield immunological or epidemiological insights. The approach applies to any biosample database labeled with orthogonal barcodes, enabling complex post-hoc analysis, for example, to study global biothreat epidemiology.

Tailor-made molecular imprints for biological event intervention

Molecular imprints, which are crosslinked architectures containing specific molecular recognition cavities for targeting compounds, have recently transitioned from in vitro diagnosis to in vivo treatment. In current application scenarios, it has become an important topic to create new biomolecular recognition pathways through molecular imprinting, thereby inhibiting the pathogenesis and regulating the development of diseases. This review starts with a pathological analysis, mainly focusing on the corresponding artificial enzymes, enzyme inhibitors and antibody mimics with enhanced functions that are created by molecular imprinting strategies. Recent advances are highlighted in the use of molecular imprints as tailor-made nanomedicines for the prevention of three major diseases: metabolic syndrome, cancer, and bacterial/viral infections.

CD74 as a prognostic and M1 macrophage infiltration marker in a comprehensive pan-cancer analysis

CD74 is a type-II transmembrane glycoprotein that has been linked to tumorigenesis. However, this association was based only on phenotypic studies, and, to date, no in-depth mechanistic studies have been conducted. In this study, combined with a multi-omics study, CD74 levels were significantly upregulated in most cancers relative to normal tissues and were found to be predictive of prognosis. Elevated CD74 expression was associated with reduced levels of mismatch-repair genes and homologous repair gene signatures in over 10 tumor types. Multiple fluorescence staining and bulk, spatial, single-cell transcriptional analyses indicated its potential as a marker for M1 macrophage infiltration in pan-cancer. In addition, CD74 expression was higher in BRCA patients responsive to conventional chemotherapy and was able to predict the prognosis of these patients. Potential CD74-activating drugs (HNHA and BRD-K55186349) were identified through molecular docking to CD74. The findings indicate activation of CD74 may have potential in tumor immunotherapy.

The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste and Optimize Patient Treatment

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.

Community cohesion looseness in gene networks reveals individualized drug targets and resistance

Community cohesion plays a critical role in the determination of an individual's health in social science. Intriguingly, a community structure of gene networks indicates that the concept of community cohesion could be applied between the genes as well to overcome the limitations of single gene-based biomarkers for precision oncology. Here, we develop community cohesion scores which precisely quantify the community ability to retain the interactions between the genes and their cellular functions in each individualized gene network. Using breast cancer as a proof-of-concept study, we measure the community cohesion score profiles of 950 case samples and predict the individualized therapeutic targets in 2-fold. First, we prioritize them by finding druggable genes present in the community with the most and relatively decreased scores in each individual. Then, we pinpoint more individualized therapeutic targets by discovering the genes which greatly contribute to the community cohesion looseness in each individualized gene network. Compared with the previous approaches, the community cohesion scores show at least four times higher performance in predicting effective individualized chemotherapy targets based on drug sensitivity data. Furthermore, the community cohesion scores successfully discover the known breast cancer subtypes and we suggest new targeted therapy targets for triple negative breast cancer (e.g. KIT and GABRP). Lastly, we demonstrate that the community cohesion scores can predict tamoxifen responses in ER+ breast cancer and suggest potential combination therapies (e.g. NAMPT and RXRA inhibitors) to reduce endocrine therapy resistance based on individualized characteristics. Our method opens new perspectives for the biomarker development in precision oncology.

Multisampling-based docking reveals Imidazolidinyl urea as a multitargeted inhibitor for lung cancer: an optimisation followed multi-simulation and in-vitro study

Lung Cancer is one of the deadliest cancers, responsible for more than 1.80 million deaths annually worldwide, and it is on the priority list of WHO. In the current scenario, when cancer cells become resistant to the drug, making it less effective leaves the patient in vulnerable conditions. To overcome this situation, researchers are constantly working on new drugs and medications that can help fight drug resistance and improve patients' outcomes. In this study, we have taken five main proteins of lung cancer, namely RSK4 N-terminal kinase, guanylate kinase, cyclin-dependent kinase 2, kinase CK2 holoenzyme, tumour necrosis factor-alpha and screened the prepared Drug Bank library with 1,55,888 compounds against all using three Glide-based docking algorithms namely HTVS, standard precision and extra precise with a docking score ranging from -5.422 to -8.432 Kcal/mol. The poses were filtered with the MM\GBSA calculations, which helped to identify Imidazolidinyl urea C11H16N8O8 (DB14075) as a multitargeted inhibitor for lung cancer, validated with advanced computations like ADMET, interaction pattern fingerprints, and optimised the compound with Jaguar, producing satisfied relative energy. All five complexes were performed with MD Simulation for 100 ns with NPT ensemble class, producing cumulative deviation and fluctuations < 2 Å and a web of intermolecular interaction, making the complexes stable. Further, the in-vitro analysis for morphological imaging, Annexin V/PI FACS assay, ROS and MMP analysis caspase3//7 activity were performed on the A549 cell line producing promising results and can be an option to treat lung cancer at a significantly cheaper state.Communicated by Ramaswamy H. Sarma.

Omics-based molecular classifications empowering in precision oncology

BACKGROUND

In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine.

CONCLUSION

This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Rapid Volumetric Bioprinting of Decellularized Extracellular Matrix Bioinks

Decellularized extracellular matrix (dECM)-based hydrogels are widely applied to additive biomanufacturing strategies for relevant applications. The extracellular matrix components and growth factors of dECM play crucial roles in cell adhesion, growth, and differentiation. However, the generally poor mechanical properties and printability have remained as major limitations for dECM-based materials. In this study, heart-derived dECM (h-dECM) and meniscus-derived dECM (Ms-dECM) bioinks in their pristine, unmodified state supplemented with the photoinitiator system of tris(2,2-bipyridyl) dichlororuthenium(II) hexahydrate and sodium persulfate, demonstrate cytocompatibility with volumetric bioprinting processes. This recently developed bioprinting modality illuminates a dynamically evolving light pattern into a rotating volume of the bioink, and thus decouples the requirement of mechanical strengths of bioprinted hydrogel constructs with printability, allowing for the fabrication of sophisticated shapes and architectures with low-concentration dECM materials that set within tens of seconds. As exemplary applications, cardiac tissues are volumetrically bioprinted using the cardiomyocyte-laden h-dECM bioink showing favorable cell proliferation, expansion, spreading, biomarker expressions, and synchronized contractions; whereas the volumetrically bioprinted Ms-dECM meniscus structures embedded with human mesenchymal stem cells present appropriate chondrogenic differentiation outcomes. This study supplies expanded bioink libraries for volumetric bioprinting and broadens utilities of dECM toward tissue engineering and regenerative medicine.

Ultrasensitive Point-of-Care Detection of Protein Markers Using an Aptamer-CRISPR/Cas12a-Regulated Liquid Crystal Sensor (ALICS)

Despite extensive efforts, point-of-care testing (POCT) of protein markers with high sensitivity and specificity and at a low cost remains challenging. In this work, we developed an aptamer-CRISPR/Cas12a-regulated liquid crystal sensor (ALICS), which achieved ultrasensitive protein detection using a smartphone-coupled portable device. Specifically, a DNA probe that contained an aptamer sequence for the protein target and an activation sequence for the Cas12a-crRNA complex was prefixed on a substrate and was released in the presence of target. The activation sequence of the DNA probe then bound to the Cas12a-crRNA complex to activate the collateral cleavage reaction, producing a bright-to-dark optical change in a DNA-functionalized liquid crystal interface. The optical image was captured by a smartphone for quantification of the target concentration. For the two model proteins, SARS-CoV-2 nucleocapsid protein (N protein) and carcino-embryonic antigen (CEA), ALICS achieved detection limits of 0.4 and 20 pg/mL, respectively, which are higher than the typical sensitivity of the SARS-CoV-2 test and the clinical CEA test. In the clinical sample tests, ALICS also exhibited superior performances compared to those of the commercial ELISA and lateral flow test kits. Overall, ALICS represents an ultrasensitive and cost-effective platform for POCT, showing a great potential for pathogen detection and disease monitoring under resource-limited conditions.

Designed Nanomaterials-Assisted Proteomics and Metabolomics Analysis for In Vitro Diagnosis

In vitro diagnosis (IVD) is pivotal in modern medicine, enabling early disease detection and treatment optimization. Omics technologies, particularly proteomics and metabolomics, offer profound insights into IVD. Despite its significance, omics analyses for IVD face challenges, including low analyte concentrations and the complexity of biological environments. In addition, the direct omics analysis by mass spectrometry (MS) is often hampered by issues like large sample volume requirements and poor ionization efficiency. Through manipulating their size, surface charge, and functionalization, as well as the nanoparticle-fluid incubation conditions, nanomaterials have emerged as a promising solution to extract biomolecules and enhance the desorption/ionization efficiency in MS detection. This review delves into the last five years of nanomaterial applications in omics, focusing on their role in the enrichment, separation, and ionization analysis of proteins and metabolites for IVD. It aims to provide a comprehensive update on nanomaterial design and application in omics, highlighting their potential to revolutionize IVD.

EBV-microRNAs as Potential Biomarkers in EBV-related Fever: A Narrative Review

At present, timely and accurate diagnosis and effective treatment of Epstein- Barr Virus (EBV) infection-associated fever remain a difficult challenge. EBV encodes 44 mature microRNAs (miRNAs) that inhibit viral lysis, adjust inflammatory response, regulate cellular apoptosis, promote tumor genesis and metastasis, and regulate tumor cell metabolism. Herein, we have collected the specific expression data of EBV-miRNAs in EBV-related fevers, including infectious mononucleosis (IM), EBVassociated hemophagocytic lymphohistiocytosis (EBV-HLH), chronic active EBV infection (CAEBV), and EBV-related tumors, and proposed the potential value of EBVmiRNAs as biomarkers to assist in the identification, diagnosis, and prognosis of EBVrelated fever, as well as therapeutic targets for drug development.

Nrf2, A Target for Precision Oncology in Cancer Prognosis and Treatment

Activating nuclear factor-erythroid 2-related factor (Nrf2), a master regulator of redox homeostasis, has been shown to suppress initiation of carcinogenesis in normal cells. However, this transcription factor has recently been reported to promote proliferation of some transformed or cancerous cells. In tumor cells, Nrf2 is prone to mutations that result in stabilization and concurrent accumulation of its protein product. A hyperactivated mutant form of Nrf2 could support the cancer cells for enhanced proliferation, invasiveness, and resistance to chemotherapeutic agents and radiotherapy, which are associated with a poor clinical outcome. Hence understanding mutations in Nrf2 would have a significant impact on the prognosis and treatment of cancer in the era of precision medicine. This perspective would provide an insight into the genetic alterations in Nrf2 and suggest the application of small molecules, RNAi, and genome editing technologies, particularly CRISR-Cas9, in therapeutic intervention of cancer in the context of the involvement of Nrf2 mutations.

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.

Promising preclinical patient-derived organoid (PDO) and xenograft (PDX) models in upper gastrointestinal cancers: progress and challenges

Gastrointestinal (GI) cancers (gastric cancer, oesophageal cancer, liver cancer, colorectal cancer, etc.) are the most common cancers with the highest morbidity and mortality in the world. The therapy for most GI cancers is difficult and is associated with a poor prognosis. In China, upper GI cancers, mainly gastric cancer (GC) and oesophageal cancer (EC), are very common due to Chinese people's characteristics, and more than half of patients are diagnosed with distant metastatic or locally advanced disease. Compared to other solid cancers, such as lung cancer and breast cancer, personalized therapies, especially targeted therapy and immunotherapy, in GC and EC are relatively lacking, leading to poor prognosis. For a long time, most studies were carried out by using in vitro cancer cell lines or in vivo cell line-derived xenograft models, which are unable to reproduce the characteristics of tumours derived from patients, leading to the possible misguidance of subsequent clinical validation. The patient-derived models represented by patient-derived organoid (PDO) and xenograft (PDX) models, known for their high preservation of patient tumour features, have emerged as a very popular platform that has been widely used in numerous studies, especially in the research and development of antitumour drugs and personalized medicine. Herein, based on some of the available published literature, we review the research and application status of PDO and PDX models in GC and EC, as well as detail their future challenges and prospects, to promote their use in basic and translational studies or personalized therapy.

Dual network analysis of transcriptome data for discovery of new therapeutic targets in non-small cell lung cancer

The drug therapy for non-small cell lung cancer (NSCLC) have always been issues of poisonous side effect, acquired drug resistance and narrow applicable population. In this study, we built a novel network analysis method (difference- correlation- enrichment- causality- node), which was based on the difference analysis, Spearman correlation network analysis, biological function analysis and Bayesian causality network analysis to discover new therapeutic target of NSCLC in the sequencing data of BEAS-2B and 7 NSCLC cell lines. Our results showed that, as a proteasome subunit coding gene in the central of cell cycle network, PSMD2 was associated with prognosis and was an independent prognostic factor for NSCLC patients. Knockout of PSMD2 inhibited the proliferation of NSCLC cells by inducing cell cycle arrest, and exhibited marked increase of cell cycle blocking protein p21, p27 and decrease of cell cycle driven protein CDK4, CDK6, CCND1 and CCNE1. IPA and molecular docking suggested bortezomib has stronger affinity to PSMD2 compared with reported targets PSMB1 and PSMB5. In vitro and In vivo experiments demonstrated the inhibitory effect of bortezomib in NSCLC with different driven mutations or with tyrosine kinase inhibitors resistance. Taken together, bortezomib could target PSMD2, PSMB1 and PSMB5 to inhibit the proteasome degradation of cell cycle check points, to block cell proliferation of NSCLC, which was potential optional drug for NSCLC patients.

Expression prevalence and dynamics of GPCR somatostatin receptors 2 and 3 as cancer biomarkers beyond NET: a paired immunohistochemistry approach

Somatostatin receptors are clinically validated GPCR biomarkers for diagnosis and treatment of various neuroendocrine tumors (NET). Among the five somatostatin receptors, SST2 and SST3 are associated with apoptosis and cell cycle arrest, making these receptor subtypes better differentiated targets in precision oncology. In this study we performed immunohistochemistry of paired tissue microarrays containing 1125 cores, representing 43 tumor types, each stained for SST2 and SST3. A 12-point immunoreactive scoring (IRS) range was used for interpretation of the staining results. We analyzed the results twice, using the conventional positivity IRS cutoffs ≥ 3 and more stringent ≥ 6. Evaluation of receptors expression dynamics was performed for tumor-nodes-metastases (TNM) defined subgroups (ovarian and hepatocellular adenocarcinomas) as a function of their tumor stage. Our results indicate that two-thirds of tested cores exhibit clinically significant expression of at least SST2 or SST3 (IRS ≥ 6). The expression prevalence of both receptors tends to decline with tumor progression. However, an unexpected upregulation of both SST2 and SST3 reemerged in metastases suggesting conserved receptors genetic potential during tumor life cycle. We suggest that SST2 and SST3 should be further explored as potential biomarkers and therapeutic tools for maximizing the efficiency of somatostatin-based precision oncology of solid tumors beyond NET.

Increased expression of individual genes in whole blood is associated with late-stage lung cancer at and close to diagnosis

Lung cancer (LC) mortality rates are still increasing globally. As survival is linked to stage, there is a need to identify markers for earlier LC diagnosis and individualized treatment. The whole blood transcriptome of LC patients represents a source of potential LC biomarkers. We compared expression of > 60,000 genes in whole blood specimens taken from LC cases at diagnosis (n = 128) and controls (n = 62) using genome-wide RNA sequencing, and identified 14 candidate genes associated with LC. High expression of ANXA3, ARG1 and HP was strongly associated with lower survival in late-stage LC cases (hazard ratios (HRs) = 2.81, 2.16 and 2.54, respectively). We validated these markers in two independent population-based studies with pre-diagnostic whole blood specimens taken up to eight years prior to LC diagnosis (n = 163 cases, 184 matched controls). ANXA3 and ARG1 expression was strongly associated with LC in these specimens, especially with late-stage LC within two years of diagnosis (odds ratios (ORs) = 3.47 and 5.00, respectively). Additionally, blood CD4 T cells, NK cells and neutrophils were associated with LC at diagnosis and improved LC discriminative ability beyond candidate genes. Our results indicate that in whole blood, increased expression levels of ANXA3, ARG1 and HP are diagnostic and prognostic markers of late-stage LC.

Comprehensive analysis of HDAC7 expression and its prognostic value in diffuse large B cell lymphoma: A review

HDAC7 loss or dysregulation may lead to B cell-based hematological malignancies. This study aimed to explore the prognostic value of HDAC7 in patients with diffuse large B cell lymphoma (DLBCL). RNA sequencing data and clinical information for HDAC7 in DLBCL were collected from the cancer genome atlas database and analyzed using R software. Paired t and Mann-Whitney U tests were used to detect differences between DLBCL and adjacent normal tissues, and the pROC software package was used to generate receiver operator characteristic curves to detect cutoff values for HDAC7. Data from paraffin-embedded specimens from the 2 groups were used for validation of external immunohistochemical staining. The tumor immunity estimation resource and integrated repository portal for tumor immune system interactions databases were used to analyze the correlation between HDAC7 and DLBCL immune cell infiltration. Survival analysis of HDAC7 in patients with DLBCL was performed using the PrognoScan database. Compared with that in normal tissues, HDAC7 mRNA was overexpressed in DLBCL. The HDAC7 immunohistochemical staining scores of stage III and IV DLBCL patients were significantly lower than those of stage I and II DLBCL patients, which was associated with shorter overall survival and disease-specific survival. In addition, the higher expression of HDAC7 may play a role in the lower level of immune infiltration in DLBCL. Downregulation of HDAC7 expression was correlated with poor prognosis and immune infiltration in DLBCL patients.

Cancer management during the COVID-19 world pandemic

Since 2019, the world has been experiencing an outbreak of a novel beta-coronavirus, severe acute respiratory syndrome coronavirus (SARS-CoV)-2. The worldwide spread of this virus has been a severe challenge for public health, and the World Health Organization declared the outbreak a public health emergency of international concern. As of June 8, 2023, the virus' rapid spread had caused over 767 million infections and more than 6.94 million deaths worldwide. Unlike previous SARS-CoV-1 and Middle East respiratory syndrome coronavirus outbreaks, the COVID-19 outbreak has led to a high death rate in infected patients; this has been caused by multiorgan failure, which might be due to the widespread presence of angiotensin-converting enzyme 2 (ACE2) receptors-functional receptors of SARS-CoV-2-in multiple organs. Patients with cancer may be particularly susceptible to COVID-19 because cancer treatments (e.g., chemotherapy, immunotherapy) suppress the immune system. Thus, patients with cancer and COVID-19 may have a poor prognosis. Knowing how to manage the treatment of patients with cancer who may be infected with SARS-CoV-2 is essential. Treatment decisions must be made on a case-by-case basis, and patient stratification is necessary during COVID-19 outbreaks. Here, we review the management of COVID-19 in patients with cancer and focus on the measures that should be adopted for these patients on the basis of the organs or tissues affected by cancer and by the tumor stage.

High-dose radiation-resistant lung cancer cells stored many functional lipid drops through JAK2/p-STAT3/FASN pathway

BACKGROUND

The understanding of radiation resistance is still unclear. This study aims to explore the new mechanism of radiation resistance in lung cancer from the perspective of lipid metabolism.

METHODS

Oil red O was used to detect the amount of lipid droplets in high-dose radiation-resistant lung cancer cells (HDRR-LCCs) and the primary lung cancer cells. Western blot analysis was used to determine the protein expression levels of key molecules related to de novo fatty acid synthesis and fatty acid transport. Orlistat was used to inhibit the de novo fatty acid synthesis. The prediction of the transcriptional regulators of fatty acid synthetase (FASN) was analyzed by bioinformatics. AZD-1480 was used to inhibit the JAK2/STAT3 pathway to observe its effects on FASN and intracellular lipid droplets. The regulation of the transcription factor p-STAT3 on the FASN gene was verified by Chip-qPCR. Finally, we used the public data of lung cancer patients to analyze the correlation between FASN and LPL gene expression with the prognosis.

RESULTS

There were more lipid drops in the HDRR-LCCs than in the primary lung cancer cells. HDRR-LCCs preferred de novo synthesis of fatty acids, and high expression of LPL homodimers indicated a high intake of extracellular fatty acids. The expression of FASN was increased in HDRR-LCCs compared with the primary lung cancer cells in a radiation-dose-dependent way, while LPL homodimers did not show such a trend. The lipid droplets, cell proliferation, and radiation resistance were decreased in HDRR-LCCs after orlistat treatment. Lipid droplets were significantly reduced, and the protein expression of FASN also decreased when using AZD-1480 to inhibit the JAK2/STAT3 pathway. The Chip-qPCR showed that p-STAT3 was the upstream regulator which binds to the promoter region of FASN. Survival analysis showed that high expression of the FASN gene was associated with a poor prognosis in lung cancer patients who received radiotherapy.

CONCLUSION

Our studies discovered that lipids deposited in HDRR-LCCs were due to endogenous de novo fatty acids synthesis and exogenous lipids uptake. JAK2/p-TAT3/FASN could be used as promising targets for radiotherapy sensitization. Our study provided a new theoretical basis for studying the mechanism of radiation resistance in lung cancer.

Signaling-induced systematic repression of miRNAs uncovers cancer vulnerabilities and targeted therapy sensitivity

Targeted therapies are effective in treating cancer, but success depends on identifying cancer vulnerabilities. In our study, we utilize small RNA sequencing to examine the impact of pathway activation on microRNA (miRNA) expression patterns. Interestingly, we discover that miRNAs capable of inhibiting key members of activated pathways are frequently diminished. Building on this observation, we develop an approach that integrates a low-miRNA-expression signature to identify druggable target genes in cancer. We train and validate our approach in colorectal cancer cells and extend it to diverse cancer models using patient-derived in vitro and in vivo systems. Finally, we demonstrate its additional value to support genomic and transcriptomic-based drug prediction strategies in a pan-cancer patient cohort from the National Center for Tumor Diseases (NCT)/German Cancer Consortium (DKTK) Molecularly Aided Stratification for Tumor Eradication (MASTER) precision oncology trial. In conclusion, our strategy can predict cancer vulnerabilities with high sensitivity and accuracy and might be suitable for future therapy recommendations in a variety of cancer subtypes.

The mitochondrial fusion-associated protein MFN2 can be used as a novel prognostic molecule for clear cell renal cell carcinoma

BACKGROUND

Mitofusin 2 (MFN2) plays an important role in many tumors, but how its role in renal clear cell carcinoma needs further research.

METHODS

In this study, we analyzed the expression of MFN2 in renal clear cell carcinoma tissues and normal kidney tissues through the Cancer Genome Atlas (TCGA) database and our clinical samples.Enrichment analysis was performed to determine MFN2-related pathways and biological functions. The correlation of MFN2 expression with immune cells was analyzed.The correlation of the expression of methylation and the methylation sites of MFN2 were analyzed by UALCAN and TCGA databases. Univariate / multivariate COX risk regression and Kaplan-Meier methods were used to determine the prognostic value of MFN2.Nomograms were drawn to predict overall survival (OS) at 1,3, and 5 years. We investigated the role of MFN2 in renal cancer cells using CCK 8, clone formation, wound healing assay, and methylase qPCR experiments.

RESULTS

MFN2 is poorly expressed in renal clear cell carcinoma compared to normal kidney tissue,and is significantly negatively associated with TNM stage, histological grade and pathological stage.MFN2 was directly associated with OS after multivariate Cox regression analysis.MFN2 shows a hypomethylation state and shows a positive correlation with multiple methylation sites.Signaling pathways through functional enrichment to B-cell receptors and oxidative stress-induced senescence.Moreover, the low expression of MFN2 was positively correlated with the degree of immune cell infiltration in a variety of immune cells.In vitro experiments showed that overexpression of MFN2 significantly inhibited the proliferation and migration of renal clear cells and promoted methylation.

CONCLUSIONS

In conclusion, MFN2 can be used as a novel prognostic marker for renal clear cell carcinoma and requires further investigation of its role in tumor development.

Synthetic Gene Circuit-Based Assay with Multilevel Switch Enables Background-Free and Absolute Quantification of Circulating Tumor DNA

Circulating tumor DNA (ctDNA) detection has found widespread applications in tumor diagnostics and treatment, where the key is to obtain accurate quantification of ctDNA. However, this remains challenging due to the issue of background noise associated with existing assays. In this work, we developed a synthetic gene circuit-based assay with multilevel switch (termed CATCH) for background-free and absolute quantification of ctDNA. The multilevel switch combining a small transcription activating RNA and a toehold switch was designed to simultaneously regulate transcription and translation processes in gene circuit to eliminate background noise. Moreover, such a multilevel switch-based gene circuit was integrated with a Cas9 nickase H840A (Cas9n) recognizer and a molecular beacon reporter to form CATCH for ctDNA detection. The CATCH can be implemented in one-pot reaction at 35 °C with virtually no background noise, and achieve robust absolute quantification of ctDNA when integrated with a digital chip (i.e., digital CATCH). Finally, we validated the clinical capability of CATCH by detecting drug-resistant ctDNA mutations from the plasma of 76 non-small cell lung cancer (NSCLC) patients, showing satisfying clinical sensitivity and specificity. We envision that the simple and robust CATCH would be a powerful tool for next-generation ctDNA detection.

SARS-CoV-2 infection as a potential risk factor for the development of cancer

The COVID-19 pandemic has a significant impact on public health and the estimated number of excess deaths may be more than three times higher than documented in official statistics. Numerous studies have shown an increased risk of severe COVID-19 and death in patients with cancer. In addition, the role of SARS-CoV-2 as a potential risk factor for the development of cancer has been considered. Therefore, in this review, we summarise the available data on the potential effects of SARS-CoV-2 infection on oncogenesis, including but not limited to effects on host signal transduction pathways, immune surveillance, chronic inflammation, oxidative stress, cell cycle dysregulation, potential viral genome integration, epigenetic alterations and genetic mutations, oncolytic effects and reactivation of dormant cancer cells. We also investigated the potential long-term effects and impact of the antiviral therapy used in COVID-19 on cancer development and its progression.

Identifying Network Biomarkers in Early Diagnosis of Hepatocellular Carcinoma via miRNA-Gene Interaction Network Analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer at the histological level. Despite the emergence of new biological technology, advanced-stage HCC remains largely incurable. The prediction of a cancer biomarker is a key problem for targeted therapy in the disease.

METHODS

We performed a miRNA-gene integrated analysis to identify differentially expressed miRNAs (DEMs) and genes (DEGs) of HCC. The DEM-DEG interaction network was constructed and analyzed. Gene ontology enrichment and survival analyses were also performed in this study.

RESULTS

By the analysis of healthy and tumor samples, we found that 94 DEGs and 25 DEMs were significantly differentially expressed in different datasets. Gene ontology enrichment analysis showed that these 94 DEGs were significantly enriched in the term "Liver" with a statistical p-value of 1.71 × 10-26. Function enrichment analysis indicated that these genes were significantly overrepresented in the term "monocarboxylic acid metabolic process" with a p-value = 2.94 × 10-18. Two sets (fourteen genes and five miRNAs) were screened by a miRNA-gene integrated analysis of their interaction network. The statistical analysis of these molecules showed that five genes (CLEC4G, GLS2, H2AFZ, STMN1, TUBA1B) and two miRNAs (hsa-miR-326 and has-miR-331-5p) have significant effects on the survival prognosis of patients.

CONCLUSION

We believe that our study could provide critical clinical biomarkers for the targeted therapy of HCC.

DoSurvive: A webtool for investigating the prognostic power of a single or combined cancer biomarker

We present DoSurvive, a user-friendly survival analysis web tool and a cancer prognostic biomarker centered database. DoSurvive is the first database that allows users to perform multivariant survival analysis for cancers with customized gene/patient list. DoSurvive offers three survival analysis methods, Log rank test, Cox regression and accelerated failure time model (AFT), for users to analyze five types of quantitative features (mRNA, miRNA, lncRNA, protein and methylation of CpG islands) with four survival types, i.e. overall survival, disease-specific survival, disease-free interval, and progression-free interval, in 33 cancer types. Notably, the implemented AFT model provides an alternative method for genes/features which failed the proportional hazard assumption in Cox regression. With the unprecedented number of survival models implemented and high flexibility in analysis, DoSurvive is a unique platform for the identification of clinically relevant targets for cancer researcher and practitioners. DoSurvive is freely available at http://dosurvive.lab.nycu.edu.tw/.

Multidimensional biomarkers for multiple system atrophy: an update and future directions

Multiple system atrophy (MSA) is a fatal progressive neurodegenerative disease. Biomarkers are urgently required for MSA to improve the diagnostic and prognostic accuracy in clinic and facilitate the development and monitoring of disease-modifying therapies. In recent years, significant research efforts have been made in exploring multidimensional biomarkers for MSA. However, currently few biomarkers are available in clinic. In this review, we systematically summarize the latest advances in multidimensional biomarkers for MSA, including biomarkers in fluids, tissues and gut microbiota as well as imaging biomarkers. Future directions for exploration of novel biomarkers and promotion of implementation in clinic are also discussed.

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms

Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.

Refinement of safety and efficacy of anti-cancer chemotherapeutics by tailoring their site-specific intracellular bioavailability through transporter modulation

Low intracellular bioavailability, off-site toxicities, and multi drug resistance (MDR) are the major constraints involved in cancer chemotherapy. Many anticancer molecules fail to become a good lead in drug discovery because of their poor site-specific bioavailability. Concentration of a molecule at target sites is largely varied because of the wavering expression of transporters. Recent anticancer drug discovery strategies are paying high attention to enhance target site bioavailability by modulating drug transporters. The level of genetic expression of transporters is an important determinant to understand their ability to facilitate drug transport across the cellular membrane. Solid carrier (SLC) transporters are the major influx transporters involved in the transportation of most anti-cancer drugs. In contrast, ATP-binding cassette (ABC) superfamily is the most studied class of efflux transporters concerning cancer and is significantly involved in efflux of chemotherapeutics resulting in MDR. Balancing SLC and ABC transporters is essential to avoid therapeutic failure and minimize MDR in chemotherapy. Unfortunately, comprehensive literature on the possible approaches of tailoring site-specific bioavailability of anticancer drugs through transporter modulation is not available till date. This review critically discussed the role of different specific transporter proteins in deciding the intracellular bioavailability of anticancer molecules. Different strategies for reversal of MDR in chemotherapy by incorporation of chemosensitizers have been proposed in this review. Targeted strategies for administration of the chemotherapeutics to the intracellular site of action through clinically relevant transporters employing newer nanotechnology-based formulation platforms have been explained. The discussion embedded in this review is timely considering the current need of addressing the ambiguity observed in pharmacokinetic and clinical outcomes of the chemotherapeutics in anti-cancer treatment regimens.

Effect of PTGES3 on the Prognosis and Immune Regulation in Lung Adenocarcinoma

Background

PTGES3 is upregulated in multiple cancer types and promotes tumorigenesis and progression. However, the clinical outcome and immune regulation of PTGES3 in lung adenocarcinoma (LUAD) are not fully understood. This study aimed to explore the expression level and prognostic value of PTGES3 and its correlation with potential immunotherapy in LUAD.

Methods

All data were obtained from several databases, including the Cancer Genome Atlas database. Firstly, gene and protein expression of PTGES3 were analyzed using Tumor Immune Estimation Resource (TIMER), R software, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA). Thereafter, survival analysis was conducted using the R software, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and Kaplan-Meier Plotter. In addition, gene alteration and mutation analyses were conducted using the cBio Cancer Genomics Portal (cBioPortal) and Catalog of Somatic Mutations in Cancer (COSMIC) databases. The molecular mechanisms associated with PTGES3 were assessed via Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), GeneMANIA, GEPIA2, and R software. Lastly, the role of PTGES3 in immune regulation in LUAD was investigated using TIMER, Tumor-Immune System Interaction Database (TISIDB), and SangerBox.

Results

The gene and protein expression of PTGES3 were elevated in LUAD tissues and compared to the normal tissues, and the high expression of PTGES3 was correlated with cancer stage and tumor grade. Survival analysis revealed that overexpression of PTGES3 was associated with poor prognosis of LUAD patients. Moreover, gene alteration and mutation analysis revealed the occurrence of several types of PTGES3 gene alterations in LUAD. Moreover, co-expression analysis and cross-analysis revealed that three genes, including CACYBP, HNRNPC, and TCP1, were correlated and interacted with PTGES3. Functional analysis of these genes revealed that PTGES3 was primarily enriched in oocyte meiosis, progesterone-mediated oocyte maturation, and arachidonic acid metabolism pathways. Furthermore, we found that PTGES3 participated in a complex immune regulation network in LUAD.

Conclusion

The current study indicated the crucial role of PTGES3 in LUAD prognosis and immune regulation. Altogether, our results suggested that PTGES3 could serve as a promising therapeutic and prognosis biomarker for the LUAD.

Cancer cell-autonomous cGAS-STING response confers drug resistance

The cGAS-STING pathway has long been recognized as playing a crucial role in immune surveillance and tumor suppression. Here, we show that when the pathway is activated in a cancer-cell-autonomous response manner, it confers drug resistance. Targeted or conventional chemotherapy drugs promoted cytosolic DNA accumulation in cancer cells, activating the cGAS-STING pathway and downstream TBK1-IRF3/NF-κB signaling. This cancer cell-intrinsic response enabled the cells to counteract drug stress, allowing treatment resistance to be acquired and maintained. Blockade of stimulator of interferon genes (STING) signaling delayed and overcame resistance in models in vitro and in vivo. This finding uncovers an alternative face of cGAS-STING signaling other than the well-reported modulation of microenvironmental immune cells. It also implies a caution for the combination of STING agonist with targeted or conventional chemotherapy drug treatment, a strategy prevailing in current clinical trials.

DNA-framework-based multidimensional molecular classifiers for cancer diagnosis

A molecular classification of diseases that accurately reflects clinical behaviour lays the foundation of precision medicine. The development of in silico classifiers coupled with molecular implementation based on DNA reactions marks a key advance in more powerful molecular classification, but it nevertheless remains a challenge to process multiple molecular datatypes. Here we introduce a DNA-encoded molecular classifier that can physically implement the computational classification of multidimensional molecular clinical data. To produce unified electrochemical sensing signals across heterogeneous molecular binding events, we exploit DNA-framework-based programmable atom-like nanoparticles with n valence to develop valence-encoded signal reporters that enable linearity in translating virtually any biomolecular binding events to signal gains. Multidimensional molecular information in computational classification is thus precisely assigned weights for bioanalysis. We demonstrate the implementation of a molecular classifier based on programmable atom-like nanoparticles to perform biomarker panel screening and analyse a panel of six biomarkers across three-dimensional datatypes for a near-deterministic molecular taxonomy of prostate cancer patients.

An eleven-gene risk model associated with lymph node metastasis predicts overall survival in lung adenocarcinoma

Lung adenocarcinoma (LUAD) occupies major causes of tumor death. Identifying potential prognostic risk genes is crucial to predict the overall survival of patients with LUAD. In this study, we constructed and proved an 11-gene risk signature. This prognostic signature divided LUAD patients into low- and high-risk groups. The model outperformed in prognostic accuracy at varying follow-up times (AUC for 3 years: 0.699, 5 years: 0.713, and 7 years: 0.716). Two GEO datasets also indicate the great accuracy of the risk signature (AUC = 782 and 771, respectively). Multivariate analysis identified 4 independent risk factors including stage N (HR 1.320, 95% CI 1.102-1.581, P = 0.003), stage T (HR 3.159, 95% CI 1.920-3.959, P < 0.001), tumor status (HR 5.688, 95% CI 3.883-8.334, P < 0.001), and the 11-gene risk model (HR 2.823, 95% CI 1.928-4.133, P < 0.001). The performance of the nomogram was good in the TCGA database (AUC = 0.806, 0.798, and 0.818 for 3-, 5- and 7-year survival). The subgroup analysis in different age, gender, tumor status, clinical stage, and recurrence stratifications indicated that the accuracy was high in different subgroups (all P < 0.05). Briefly, our work established an 11-gene risk model and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of LUAD patients for clinicians.

Dual AuNPs detecting probe enhanced the NanoSPR effect for the high-throughput detection of the cancer microRNA21 biomarker

The microRNA21 (miR-21), a specific tumor biomarker, is crucial for the diagnosis of several cancer types, and investigation of its overexpression pattern is important for cancer diagnosis. Herein, we report a low-cost, rapid, ultrasensitive, and convenient biosensing strategy for the detection of miR-21 using a nanoplasmonic array chip coupled with gold nanoparticles (AuNPs). This sensing platform combines the surface plasmon resonance effect of nanoplasmonics (NanoSPR) and the localized surface plasmon resonance (LSPR) effect, which allows the real-time monitoring of the subtle optical density (OD) changes caused by the variations in the dielectric constant in the process of the hybridization of the target miRNA. Using this method, the miRNA achieves a broad detection range from 100 aM to 1 μM, and with a limit of detection (LoD) of 1.85 aM. Furthermore, this assay also has a single-base resolution to discriminate the highly homologous miRNAs. More importantly, this platform has high throughput characteristics (96 samples can be detected simultaneously). This strategy exhibits more than 86.5 times enhancement in terms of sensitivity compared to that of traditional biosensors.

Comprehensive pan-cancer analysis of STAT3 as a prognostic and immunological biomarker

Numerous studies have indicated that STAT3 plays a key role in promoting oncogenesis and it is considered a potential therapeutic target for cancer treatment; however, there are no reports on STAT3 using pan-cancer analysis. Therefore, it is important to investigate the role of STAT3 in different types of tumors using pan-cancer analysis. In the present study, we used multiple databases to comprehensively analyze the relationship between STAT3 expression and prognosis, different stages of patients with cancer, investigate the clinical value of STAT3 in predicting prognosis, and the relationship between STAT3 genetic alteration and prognosis, drug sensitivity, and STAT3 expression, to determine whether STAT3 participates in tumor immunity, to provide a rationale for STAT3 as a treatment target for a broad-spectrum malignancies. Our results indicate that STAT3 can serve as a prognostic, sensitivity prediction biomarker and a target for immunotherapy, which has been of great value for pan-cancer treatment. Overall, we found that STAT3 significantly predicted cancer prognosis, drug resistance, and immunotherapy, providing a rationale for further experimental studies.

Ratiometric electrochemical OR gate assay for NSCLC-derived exosomes

Non-small cell lung cancer (NSCLC) is the most common pathological type of LC and ranks as the leading cause of cancer deaths. Circulating exosomes have emerged as a valuable biomarker for the diagnosis of NSCLC, while the performance of current electrochemical assays for exosome detection is constrained by unsatisfactory sensitivity and specificity. Here we integrated a ratiometric biosensor with an OR logic gate to form an assay for surface protein profiling of exosomes from clinical serum samples. By using the specific aptamers for recognition of clinically validated biomarkers (EpCAM and CEA), the assay enabled ultrasensitive detection of trace levels of NSCLC-derived exosomes in complex serum samples (15.1 particles μL^-1 within a linear range of 10²-10⁸ particles μL^-1). The assay outperformed the analysis of six serum biomarkers for the accurate diagnosis, staging, and prognosis of NSCLC, displaying a diagnostic sensitivity of 93.3% even at an early stage (Stage I). The assay provides an advanced tool for exosome quantification and facilitates exosome-based liquid biopsies for cancer management in clinics.

Phage vs. Phage: Direct Selections of Sandwich Binding Pairs

The sandwich format immunoassay is generally more sensitive and specific than more common assay formats, including direct, indirect, or competitive. A sandwich assay, however, requires two receptors to bind non-competitively to the target analyte. Typically, pairs of antibodies (Abs) or antibody fragments (Fabs) that are capable of forming a sandwiching with the target are identified through a slow, guess-and-check method with panels of candidate binding partners. Additionally, sandwich assays that are reliant on commercial antibodies can suffer from changes to reagent quality outside the researchers' control. This report presents a reimagined and simplified phage display selection protocol that directly identifies sandwich binding peptides and Fabs. The approach yielded two sandwich pairs, one peptide-peptide and one Fab-peptide sandwich for the cancer and Parkinson's disease biomarker DJ-1. Requiring just a few weeks to identify, the sandwich pairs delivered apparent affinity that is comparable to other commercial peptide and antibody sandwiches. The results reported here could expand the availability of sandwich binding partners for a wide range of clinical biomarker assays.

High GTSE1 expression promotes cell proliferation, metastasis and cisplatin resistance in ccRCC and is associated with immune infiltrates and poor prognosis

Background: Clear cell renal cell carcinoma is the most common and fatal form of kidney cancer, accounting for 80% of new cases. Although it has been reported that GTSE1 is highly expressed in a variety of tumors and associated with malignant progression and poor clinical prognosis, its clinical significance, correlations with immune cell infiltration and biological function in ccRCC are still poorly understood.

Methods: The gene expression, clinicopathological features, and clinical significance of GTSE1 were analyzed using multiple databases, including TCGA, GEO, TIMER, and UALCAN Kaplan–Meier survival analysis, gene set enrichment analysis gene ontology enrichment Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed. Tumor-infiltrating immune cells and immunomodulators were extracted and analyzed using TCGA-KIRC profiles. Protein‒protein interactions were built using the STRING website. The protein level of GTSE1 in ccRCC patients was detected by immunohistochemistry using a ccRCC tissue chip. Finally, MTT assays, colony-formation assays, cell flow cytometry analyses, EdU-staining assays, wound-healing assays, and transwell migration and invasion assays were conducted to assess the biological function of GTSE1 in vitro.

Results: GTSE1 was overexpressed in ccRCC tissues and cells, and GTSE1 overexpression was associated with adverse clinical-pathological factors and poor clinical prognosis. Meanwhile, the functional enrichment analysis indicated that GTSE1 and its coexpressed genes were mainly related to the cell cycle, DNA replication, and immunoreaction, such as T-cell activation and innate immune response, through multiple signaling pathways, including the P53 signaling pathway and T-cell receptor signaling pathway. Furthermore, we observed a significant relationship between GTSE1 expression and the levels of infiltrating immune cells in ccRCC. Biological functional studies demonstrated that GTSE1 could promote the malignant progression of ccRCC by promoting cell proliferation, cell cycle transition, migration, and invasion capacity and decreasing the sensitivity of ccRCC cells to cisplatin.

Conclusion: Our results indicate that GTSE1, serving as a potential oncogene, can promote malignant progression and cisplatin resistance in ccRCC. Additionally, high GTSE1 expression contributes to an increased level of immune cell infiltration and is associated with a worse prognosis, providing a potential target for tumor therapy in ccRCC.

Association of COVID-19 with Comorbidities: An Update

Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) which was identified in Wuhan, China in December 2019 and jeopardized human lives. It spreads at an unprecedented rate worldwide, with serious and still-unfolding health conditions and economic ramifications. Based on the clinical investigations, the severity of COVID-19 appears to be highly variable, ranging from mild to severe infections including the death of an infected individual. To add to this, patients with comorbid conditions such as age or concomitant illnesses are significant predictors of the disease's severity and progression. SARS-CoV-2 enters inside the host cells through ACE2 (angiotensin converting enzyme2) receptor expression; therefore, comorbidities associated with higher ACE2 expression may enhance the virus entry and the severity of COVID-19 infection. It has already been recognized that age-related comorbidities such as Parkinson's disease, cancer, diabetes, and cardiovascular diseases may lead to life-threatening illnesses in COVID-19-infected patients. COVID-19 infection results in the excessive release of cytokines, called "cytokine storm", which causes the worsening of comorbid disease conditions. Different mechanisms of COVID-19 infections leading to intensive care unit (ICU) admissions or deaths have been hypothesized. This review provides insights into the relationship between various comorbidities and COVID-19 infection. We further discuss the potential pathophysiological correlation between COVID-19 disease and comorbidities with the medical interventions for comorbid patients. Toward the end, different therapeutic options have been discussed for COVID-19-infected comorbid patients.

PRDX1 stimulates non-small-cell lung carcinoma to proliferate via the Wnt/β-Catenin signaling

BACKGROUND

Previous studies have shown that PRDX1 is upregulated in some types of malignant tumors. The role of PRDX1 in non-small-cancer lung carcinoma (NSCLC) remains unclear. This study aims to identify the role of PRDX1 in influencing in-vitro biological functions of NSCLC and the molecular mechanism.

METHODS

We collected 50 cases of fresh NSCLC and adjacent non-tumoral tissues for detecting differential expressions of PRDX1 by quantitative real-time polymerase chain reaction (qRT-PCR). Survival time of NSCLC patients, defined as the period from the operation to the latest follow-up or death due to recurrence or metastasis, was recorded for assessing the relationship between PRDX1 and prognosis in NSCLC. Using lentivirus transfection, PRDX1 level was downregulated in NSCLC cells. Subsequently, proliferative and apoptotic abilities, and expression levels of vital genes in the Wnt/β-Catenin signaling were examined. Finally, the significance of activated Wnt/β-Catenin signaling during PRDX1-regulated NSCLC proliferation was explored.

RESULTS

Using GEPIA database and NSCLC tissues we collected, PRDX1 was detected to be upregulated in NSCLC samples than controls. PRDX1 level was related to tumor staging and prognosis in NSCLC. Knockdown of PRDX1 attenuated proliferative ability and stimulated apoptosis in NSCLC. Protein levels of Wnt5A was downregulated in H1299 and SPC-A1 cells with PRDX1 knockdown. Overexpression of β-Catenin enhanced proliferative ability and inhibited apoptosis in NSCLC cells with PRDX1 knockdown.

CONCLUSIONS

PRDX1 is upregulated in NSCLC samples, and linked to tumor staging and prognosis. It stimulates NSCLC to proliferate by activating the Wnt/β-Catenin signaling.

Using patient-derived organoids to predict locally advanced or metastatic lung cancer tumor response: A real-world study

Predicting the clinical response to chemotherapeutic or targeted treatment in patients with locally advanced or metastatic lung cancer requires an accurate and affordable tool. Tumor organoids are a potential approach in precision medicine for predicting the clinical response to treatment. However, their clinical application in lung cancer has rarely been reported because of the difficulty in generating pure tumor organoids. In this study, we have generated 214 cancer organoids from 107 patients, of which 212 are lung cancer organoids (LCOs), primarily derived from malignant serous effusions. LCO-based drug sensitivity tests (LCO-DSTs) for chemotherapy and targeted therapy have been performed in a real-world study to predict the clinical response to the respective treatment. LCO-DSTs accurately predict the clinical response to treatment in this cohort of patients with advanced lung cancer. In conclusion, LCO-DST is a promising precision medicine tool in treating of advanced lung cancer.

Integrating oculomics with genomics reveals imaging biomarkers for preventive and personalized prediction of arterial aneurysms

Objective

Arterial aneurysms are life-threatening but usually asymptomatic before requiring hospitalization. Oculomics of retinal vascular features (RVFs) extracted from retinal fundus images can reflect systemic vascular properties and therefore were hypothesized to provide valuable information on detecting the risk of aneurysms. By integrating oculomics with genomics, this study aimed to (i) identify predictive RVFs as imaging biomarkers for aneurysms and (ii) evaluate the value of these RVFs in supporting early detection of aneurysms in the context of predictive, preventive and personalized medicine (PPPM).

Methods

This study involved 51,597 UK Biobank participants who had retinal images available to extract oculomics of RVFs. Phenome-wide association analyses (PheWASs) were conducted to identify RVFs associated with the genetic risks of the main types of aneurysms, including abdominal aortic aneurysm (AAA), thoracic aneurysm (TAA), intracranial aneurysm (ICA) and Marfan syndrome (MFS). An aneurysm-RVF model was then developed to predict future aneurysms. The performance of the model was assessed in both derivation and validation cohorts and was compared with other models employing clinical risk factors. An RVF risk score was derived from our aneurysm-RVF model to identify patients with an increased risk of aneurysms.

Results

PheWAS identified a total of 32 RVFs that were significantly associated with the genetic risks of aneurysms. Of these, the number of vessels in the optic disc ('ntreeA') was associated with both AAA (β = -0.36, P = 6.75e-10) and ICA (β = -0.11, P = 5.51e-06). In addition, the mean angles between each artery branch ('curveangle_mean_a') were commonly associated with 4 MFS genes (FBN1: β = -0.10, P = 1.63e-12; COL16A1: β = -0.07, P = 3.14e-09; LOC105373592: β = -0.06, P = 1.89e-05; C8orf81/LOC441376: β = 0.07, P = 1.02e-05). The developed aneurysm-RVF model showed good discrimination ability in predicting the risks of aneurysms. In the derivation cohort, the C-index of the aneurysm-RVF model was 0.809 [95% CI: 0.780-0.838], which was similar to the clinical risk model (0.806 [0.778-0.834]) but higher than the baseline model (0.739 [0.733-0.746]). Similar performance was observed in the validation cohort, with a C-index of 0.798 (0.727-0.869) for the aneurysm-RVF model, 0.795 (0.718-0.871) for the clinical risk model and 0.719 (0.620-0.816) for the baseline model. An aneurysm risk score was derived from the aneurysm-RVF model for each study participant. The individuals in the upper tertile of the aneurysm risk score had a significantly higher risk of aneurysm compared to those in the lower tertile (hazard ratio = 17.8 [6.5-48.8], P = 1.02e-05).

Conclusion

We identified a significant association between certain RVFs and the risk of aneurysms and revealed the impressive capability of using RVFs to predict the future risk of aneurysms by a PPPM approach. Our finds have great potential to support not only the predictive diagnosis of aneurysms but also a preventive and more personalized screening plan which may benefit both patients and the healthcare system.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00315-7.